A modern data warehouse lets you bring together all your data at any scale easily, and to get insights through analytical dashboards, operational reports, or advanced analytics for all your users.
Big Data Adavnced Analytics on Microsoft AzureMark Tabladillo
This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
What are the Business Benefits of Microsoft AzureChris Roche
This document outlines the business benefits of Microsoft Azure, including its strong security features with data centers like spy movie facilities, cost savings from no longer needing to replace servers, scalability to flexibly adjust needs, ability to use hybrid cloud and on-premise resources, fast speed, disaster recovery by backing up to the cloud, and compliance with security and privacy demands.
Azure SQL Database Managed Instance is a new flavor of Azure SQL Database that is a game changer. It offers near-complete SQL Server compatibility and network isolation to easily lift and shift databases to Azure (you can literally backup an on-premise database and restore it into a Azure SQL Database Managed Instance). Think of it as an enhancement to Azure SQL Database that is built on the same PaaS infrastructure and maintains all it's features (i.e. active geo-replication, high availability, automatic backups, database advisor, threat detection, intelligent insights, vulnerability assessment, etc) but adds support for databases up to 35TB, VNET, SQL Agent, cross-database querying, replication, etc. So, you can migrate your databases from on-prem to Azure with very little migration effort which is a big improvement from the current Singleton or Elastic Pool flavors which can require substantial changes.
AI for an intelligent cloud and intelligent edge: Discover, deploy, and manag...James Serra
Discover, manage, deploy, monitor – rinse and repeat. In this session we show how Azure Machine Learning can be used to create the right AI model for your challenge and then easily customize it using your development tools while relying on Azure ML to optimize them to run in hardware accelerated environments for the cloud and the edge using FPGAs and Neural Network accelerators. We then show you how to deploy the model to highly scalable web services and nimble edge applications that Azure can manage and monitor for you. Finally, we illustrate how you can leverage the model telemetry to retrain and improve your content.
Azure SQL Database & Azure SQL Data WarehouseMohamed Tawfik
This document provides an overview of Microsoft Azure Data Services and Azure SQL Database. It discusses Infrastructure as a Service (IaaS) versus Platform as a Service (PaaS), and highlights the opportunities in the Linux database market. It also discusses Microsoft's commitment to customer choice and partnerships with companies like Red Hat. The remainder of the document focuses on features of Azure SQL Database, including an overview of the DTU and vCore purchasing models, managed instances, backup and recovery, high availability options, elastic scalability, and data sync capabilities.
Data saturday Oslo Azure Purview Erwin de KreukErwin de Kreuk
Azure Purview provides unified data governance capabilities including automated data discovery, classification, and lineage visualization. It helps organizations overcome data governance silos, comply with regulations, and increase data agility. The key components of Azure Purview include the Data Map for automated metadata extraction and lineage, the Data Catalog for data discovery and governance, and Insights for monitoring data usage. It supports governance of data across cloud and on-premises environments in a serverless and fully managed platform.
This document discusses using Azure HDInsight for big data applications. It provides an overview of HDInsight and describes how it can be used for various big data scenarios like modern data warehousing, advanced analytics, and IoT. It also discusses the architecture and components of HDInsight, how to create and manage HDInsight clusters, and how HDInsight integrates with other Azure services for big data and analytics workloads.
How Azure Databricks helped make IoT Analytics a Reality with Janath Manohara...Databricks
At Lennox International, we have thousands of IoT connected devices streaming data into the Azure platform with a minute level polling interval. The challenge was to use these data sets, combine with external data sources such as weather, and predict equipment failure with high levels of accuracy along with their influencing patterns and parameters. Previously the team was using a combination of on-premise and desktop tools to run algorithms on a sample set of devices. The result was low accuracy levels (around 65%) on a process that took more than 6 hours.
The team had to work through several data orchestration challenges and identify a machine learning platform which enabled them to collaborate between our engineering SME’s, Data Engineers and Data Scientists. The team decided to use Azure Databricks to build the data engineering pipelines, appropriate machine learning models and extract predictions using PySpark. To enhance the sophistication of the learning, the team worked on a variety of Spark ML models such as Gradient Boosted Trees and Random Forest. The team also implemented stacking, ensemble methods using H2O driverless AI and sparkling water on Azure Databricks clusters, which can scale up to 1000 cores.
Join us in this session and see how this resulted in models that run in 40 minutes with minimal tuning and predict failures with accuracy of about 90%.
This document provides an overview of Azure HDInsight and options for building data lakes in the cloud. It discusses HDInsight's advantages like preserving existing Hadoop investments. It also covers Azure's data landscape including storage, streaming, ETL, and orchestration options. Key technologies are compared like Hive, Spark, and Storm. Best practices are shared around monitoring, security, data transfer, and disaster recovery.
Azure Machine Learning Services provides an end-to-end, scalable platform for operationalizing machine learning models. It allows users to deploy models everywhere from containers and Kubernetes to SQL Datawarehouse and Cosmos DB. It also offers tools to boost data science productivity, increase experimentation, and automate model retraining. The platform seamlessly integrates with Azure services and is built to deploy models globally at scale with high availability and low latency.
The document discusses how organizations can leverage cloud, data, and AI to gain competitive advantages. It notes that 80% of organizations now adopt cloud-first strategies, AI investment increased 300% in 2017, and data is expected to grow dramatically. The document promotes Microsoft's cloud-based analytics services for harnessing data at scale from various sources and types. It provides examples of how companies have used these services to improve customer experience, reduce costs, speed up insights, and gain operational efficiencies.
ITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data LakeITCamp
ML.NET is an open source, machine learning framework built in .NET and runs on Windows, Linux and macOS. It allows developers to integrate custom machine learning into their applications without any prior expertise in developing or tuning machine learning models. Enhance your .NET apps with sentiment analysis, price prediction, fraud detection and more using custom models built with ML.NET
In this Session, Andy will show not only the core of ML.NET but best practices around Azure Data Lake and data in general when using .NET
Azure SQL Database is a relational database-as-a-service hosted in the Azure cloud that reduces costs by eliminating the need to manage virtual machines, operating systems, or database software. It provides automatic backups, high availability through geo-replication, and the ability to scale performance by changing service tiers. Azure Cosmos DB is a globally distributed, multi-model database that supports automatic indexing, multiple data models via different APIs, and configurable consistency levels with strong performance guarantees. Azure Redis Cache uses the open-source Redis data structure store with managed caching instances in Azure for improved application performance.
HA/DR options with SQL Server in Azure and hybridJames Serra
What are all the high availability (HA) and disaster recovery (DR) options for SQL Server in a Azure VM (IaaS)? Which of these options can be used in a hybrid combination (Azure VM and on-prem)? I will cover features such as AlwaysOn AG, Failover cluster, Azure SQL Data Sync, Log Shipping, SQL Server data files in Azure, Mirroring, Azure Site Recovery, and Azure Backup.
Microsoft azure infrastructure essentials course manualmichaeldejene4
This document provides an overview of a 3-day Microsoft Azure Infrastructure Essentials training course. The course covers Azure network services, compute, storage, backup, and Active Directory. It includes demonstrations and hands-on labs to develop skills in implementing Azure solutions. The course modules cover Azure management tools, virtual networks, virtual machines, storage, disaster recovery, and Active Directory. Upon completing the course, students will be able to manage Azure subscriptions using various tools and deploy and configure infrastructure components in Azure.
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
Overview of Microsoft Appliances: Scaling SQL Server to Hundreds of TerabytesJames Serra
Learn how SQL Server can scale to HUNDREDS of terabytes for BI solutions. This session will focus on Fast Track Solutions and Appliances, Reference Architectures, and Parallel Data Warehousing (PDW). Included will be performance numbers and lessons learned on a PDW implementation and how a successful BI solution was built on top of it using SSAS.
The cloud is all the rage. Does it live up to its hype? What are the benefits of the cloud? Join me as I discuss the reasons so many companies are moving to the cloud and demo how to get up and running with a VM (IaaS) and a database (PaaS) in Azure. See why the ability to scale easily, the quickness that you can create a VM, and the built-in redundancy are just some of the reasons that moving to the cloud a “no brainer”. And if you have an on-prem datacenter, learn how to get out of the air-conditioning business!
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
The document discusses several demos of hardware accelerated machine learning inference:
- A heavy edge demo showing hardware accelerated inferencing on the edge using a Minnow Board.
- A drone app creation demo.
- A vision AI developer kit demo.
The document discusses the intelligent edge and hybrid cloud computing. It defines the intelligent edge as where data is created and processed outside traditional centralized data centers. It predicts that by 2025, 75% of enterprise data will be created and processed at the edge. It then provides an overview of different Azure products and solutions for intelligent edge computing, including Azure Sphere, IoT Edge, Stack Edge, and Stack Hub. It discusses how these products bring cloud services and capabilities to the edge through appliances, gateways, and on-premises servers to enable hybrid cloud solutions.
Liberati dal sovraccarico e dalle limitazioni dell’infrastruttura locale. Sfrutta risorse illimitate per ottenere scalabilità per i processi HPC (High Performance Computing), per analizzare dati su vasta scala, eseguire simulazioni e modelli finanziari e sperimentare riducendo il tempo di immissione sul mercato.
Machine Learning Inference at the EdgeJulien SIMON
Machine Learning works by using powerful algorithms to discover patterns in data and construct complex mathematical models using these patterns. Once the model is built, you perform inference by applying new data to the trained model to make predictions for your application. Building and training ML models require massive computing resources so it is a natural fit for the cloud. But, inference takes a lot less computing power and is typically done in real-time when new data is available, so getting inference results with very low latency is important to making sure your applications can respond quickly to local events. AWS Greengrass ML inference gives you the best of both worlds. You use ML models that are built and trained in the cloud and you deploy and run ML inference locally on connected devices. For example, autonomous cars need to identify road signs in real time, drones need to recognize objects with or without network connectivity.
IoTSummit: Create iot devices connected or on the edge using ai and mlMarco Dal Pino
This document summarizes an IoT presentation about Azure IoT Edge. It discusses Azure IoT Edge's capabilities including running AI models and containers at the edge, deploying cognitive services containers, adding resiliency with Kubernetes, and monitoring edge devices. It also previews new IoT Edge certified edge servers and gateways from Nvidia and demonstrates logging device data in real-time.
DataPalooza at the San Francisco Loft: In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
Machine Learning inference at the EdgeJulien SIMON
This document discusses machine learning inference at the edge using Apache MXNet and AWS services. It begins with an overview of challenges with deep learning at the edge due to resource constraints and network connectivity. It then discusses Apache MXNet and how it can be used for flexible experimentation in the cloud, scalable training in the cloud, and good prediction performance at the edge by supporting different hardware. The document outlines options for predicting using cloud-based services like AWS Lambda and SageMaker endpoints or device-based prediction using tools like AWS Greengrass. It concludes by introducing AWS DeepLens, a deep learning enabled video camera, and providing resources for further information.
A journay to do AI research in the cloud.pdfLiang Yan
This document discusses the author's journey doing AI research in the cloud using open-source tools. It provides an overview of AI concepts and frameworks like PyTorch. It then describes a project called Habitat that predicts deep learning training times in the cloud. Next, it discusses AI accelerators like GPUs, distributed training, and considerations for AI cloud implementation and providers. The author shares lessons learned around dependencies, costs, and setup experiences with various cloud platforms.
This document discusses the partnership between Microsoft Azure and GE's Predix platform for industrial IoT. For Microsoft, the partnership will help existing industrial customers build and operate IIoT solutions using Azure's capabilities in artificial intelligence, data analytics, and security. For GE, Predix will benefit from Azure's large global footprint and hybrid cloud capabilities. The combination of Predix and Azure aims to bridge the gap between operational technology and information technology for industrial customers worldwide.
As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages and miss out on having fun.
In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
AMF304-Optimizing Design and Engineering Performance in the Cloud for Manufac...Amazon Web Services
Manufacturing companies in all sectors—including automotive, aerospace, semiconductor, and industrial manufacturing—rely on design and engineering software in their product development processes. These computationally-intensive applications—such as computer-aided design and engineering (CAD and CAE), electronic design automation (EDA), other performance-critical applications—require immense scale and orchestration to meet the demands of today’s manufacturing requirements. In this session, you learn how to achieve the maximum possible performance and throughput from design and engineering workloads running on Amazon EC2, elastic GPUs, and managed services such as AWS Batch and Amazon AppStream 2.0. We demonstrate specific optimization techniques and share samples on how to accelerate batch and interactive workloads on AWS. We also demonstrate how to extend and migrate on-premises, high performance compute workloads with AWS, and use a combination of On-Demand Instances, Reserved Instances, and Spot Instances to minimize costs.
This document discusses high performance computing (HPC) on Microsoft Azure. It begins with an overview of the HPC opportunity in the cloud, highlighting how the cloud provides elasticity and scale to accommodate variable computing demands. It then outlines Azure's value proposition for HPC, including its productive, trusted and hybrid capabilities. The document reviews the various HPC resources available on Azure like VMs, GPUs, and Cray supercomputers. It also discusses solutions for HPC like Azure Batch, Azure Machine Learning Compute, Azure CycleCloud and Avere vFXT. Example industry use cases are provided for automotive, financial services, manufacturing, media/entertainment and oil/gas. The summary reiterates that Azure is uniquely positioned
The document is the meeting notes from the Brisbane Azure User Group for August 2019. It includes:
- Upcoming presentations for the group from August to November 2019 on various Azure topics.
- Announcements of new and updated Azure services, including GitHub Actions, Azure Data Share, Azure Monitor for containers, Dedicated Host preview, Bot Framework updates, and Azure Security Center for IoT general availability.
- Information on lowered pricing for Azure Archive Storage and new NVv4 and HBv2 Azure virtual machines.
- Reminders about the Azure Australia Slack team and the BAUG YouTube channel.
- Updates on on-demand Azure training from Microsoft, Pluralsight, and OpsGility.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
The number of internet-connected devices is growing exponentially, enabling an increasing number of edge applications in environments such as smart cities, retail, and industry 4.0. These intelligent solutions often require processing large amounts of data, running models to enable image recognition, predictive analytics, autonomous systems, and more. Increasing system workloads and data processing capacity at the edge is essential to minimize latency, improve responsiveness, and reduce network traffic back to data centers. Purpose-built systems such as Supermicro’s short-depth, multi-node SuperEdge, powered by 3rd Gen Intel® Xeon® Scalable processors, increase compute and I/O density at the edge and enable businesses to further accelerate innovation.
Join this webinar to discover new insights in edge-to-cloud infrastructures and learn how Supermicro SuperEdge multi-node solutions leverage data center scale, performance, and efficiency for 5G, IoT, and Edge applications.
This document summarizes a presentation given at AWS re:Invent 2017 about optimizing design and engineering performance in the cloud. The presentation covered deploying CAD/CAE/EDA applications in the cloud, optimizing storage and compute, managing technical software, enabling remote graphics and collaboration. It also included a case study from Hiroshi Kobayashi of Western Digital describing their use of AWS for CPU and GPU clusters to optimize product design.
If you're like most of the world, you're on an aggressive race to implement machine learning applications and on a path to get to deep learning. If you can give better service at a lower cost, you will be the winners in 2030. But infrastructure is a key challenge to getting there. What does the technology infrastructure look like over the next decade as you move from Petabytes to Exabytes? How are you budgeting for more colossal data growth over the next decade? How do your data scientists share data today and will it scale for 5-10 years? Do you have the appropriate security, governance, back-up and archiving processes in place? This session will address these issues and discuss strategies for customers as they ramp up their AI journey with a long term view.
Similar to AI for Intelligent Cloud and Intelligent Edge:Discover, Deploy, and Manage with Azure ML Services (20)
Keynote at Advantech's AI+Smart Manufacturing event. Shared the AI trend in smart manufacturing as well as a demo regarding how to use Azure Cognitive Services to empower employees and customers.
In this sessions, we introduce how to create an automatic devices provision solution with Azure IoT and .NET Core, and how to deploy the IoT solutions at scale.
The IoT Hub Device Provisioning Service is a helper service for IoT Hub that enables zero-touch, just-in-time provisioning to the right IoT hub without requiring human intervention, allowing customers to provision millions of devices in a secure and scale-able manner.
This session described how to do IoT device provisioning in a global scale including a real case demonstration.
This document discusses using computer vision and sensors for environmental detection in an ideal IoT solution. It describes capturing images with cameras to monitor current conditions and detect situations like air pollution. Deep learning and OpenCV can be used to analyze images at the edge or in the cloud using Azure services like Custom Vision and Cognitive Services. A demo is provided of a smart IoT solution that detects air pollution by training a model with images and deploying it to edge devices.
Details of description part II: Describing images in practice - Tech Forum 2024BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
Performance Budgets for the Real World by Tammy EvertsScyllaDB
Performance budgets have been around for more than ten years. Over those years, we’ve learned a lot about what works, what doesn’t, and what we need to improve. In this session, Tammy revisits old assumptions about performance budgets and offers some new best practices. Topics include:
• Understanding performance budgets vs. performance goals
• Aligning budgets with user experience
• Pros and cons of Core Web Vitals
• How to stay on top of your budgets to fight regressions
An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)
How Netflix Builds High Performance Applications at Global ScaleScyllaDB
We all want to build applications that are blazingly fast. We also want to scale them to users all over the world. Can the two happen together? Can users in the slowest of environments also get a fast experience? Learn how we do this at Netflix: how we understand every user's needs and preferences and build high performance applications that work for every user, every time.
The DealBook is our annual overview of the Ukrainian tech investment industry. This edition comprehensively covers the full year 2023 and the first deals of 2024.
Coordinate Systems in FME 101 - Webinar SlidesSafe Software
If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights.
During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to:
- Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value
- Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems
- Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors
- Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported
- Look Ahead: Gain insights into where FME is headed with coordinate systems in the future
Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!
Video traffic on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research and of industrial networked multimedia services certainly was the HTTP Adaptive Streaming (HAS) technique. This resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) which, together with HTTP Live Streaming (HLS), is widely used for multimedia delivery in today’s networks. Existing challenges in multimedia systems research deal with the trade-off between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, latency), and (iii) quality of experience (QoE). Optimizing towards one aspect usually negatively impacts at least one of the other two aspects if not both. This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry. In this talk, we will present selected novel approaches and research results of the first year of the ATHENA CD Lab’s operation. We will highlight HAS-related research on (i) multimedia content provisioning (machine learning for video encoding); (ii) multimedia content delivery (support of edge processing and virtualized network functions for video networking); (iii) multimedia content consumption and end-to-end aspects (player-triggered segment retransmissions to improve video playout quality); and (iv) novel QoE investigations (adaptive point cloud streaming). We will also put the work into the context of international multimedia systems research.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/07/intels-approach-to-operationalizing-ai-in-the-manufacturing-sector-a-presentation-from-intel/
Tara Thimmanaik, AI Systems and Solutions Architect at Intel, presents the “Intel’s Approach to Operationalizing AI in the Manufacturing Sector,” tutorial at the May 2024 Embedded Vision Summit.
AI at the edge is powering a revolution in industrial IoT, from real-time processing and analytics that drive greater efficiency and learning to predictive maintenance. Intel is focused on developing tools and assets to help domain experts operationalize AI-based solutions in their fields of expertise.
In this talk, Thimmanaik explains how Intel’s software platforms simplify labor-intensive data upload, labeling, training, model optimization and retraining tasks. She shows how domain experts can quickly build vision models for a wide range of processes—detecting defective parts on a production line, reducing downtime on the factory floor, automating inventory management and other digitization and automation projects. And she introduces Intel-provided edge computing assets that empower faster localized insights and decisions, improving labor productivity through easy-to-use AI tools that democratize AI.
Blockchain and Cyber Defense Strategies in new genre timesanupriti
Explore robust defense strategies at the intersection of blockchain technology and cybersecurity. This presentation delves into proactive measures and innovative approaches to safeguarding blockchain networks against evolving cyber threats. Discover how secure blockchain implementations can enhance resilience, protect data integrity, and ensure trust in digital transactions. Gain insights into cutting-edge security protocols and best practices essential for mitigating risks in the blockchain ecosystem.
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
Fluttercon 2024: Showing that you care about security - OpenSSF Scorecards fo...Chris Swan
Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge.
You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter.
The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.
Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Em...Erasmo Purificato
Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)
19. Understanding the Edge: Heavy Edge vs Light Edge
Cloud: Azure Heavy Edge Light Edge
Description
An Azure host that
spans from CPU to GPU
and FPGA VMs
A server with slots to insert CPUs, GPUs, and FPGAs or a X64 or ARM system that needs to be
plugged in to work
A Sensor with a SOC (ARM CPU, NNA, MCU) and memory that
can operate on batteries
Example
DSVM / ACI / AKS /
Batch AI
- DataBox Edge
- HPE
- Azure Stack
- DataBox Edge - Industrial PC
-Video Gateway
-DVR
-Mobile Phones
-VAIDK
-Mobile Phones
-IP Cameras
-Azure Sphere
- Appliances
What runs
model
CPU,GPU or FPGA CPU,GPU or FPGA CPU, GPU x64 CPU Multi-ARM CPU
Hw accelerated
NNA
CPU/GPU MCU
24. Why Intelligent Edge?
High-speed data processing,
analytics and shorter response
times are more essential than ever.
Intelligent Cloud
• Business agility and scalability: unlimited computing
power available on demand.
Intelligent Edge
• Can handle priority-one tasks locally
even without cloud connection.
• Can handle generated data that is too
large to pull rapidly from the cloud.
• Enables real-time processing through
intelligence in or near to local devices.
• Flexibility to accommodate data privacy related
requirements.
26. Challenges of Running AI on the Edge
• Reduced Compute Power
• No common HW abstraction for
NN
• Driver version fragmentation
• Need familiarity with every
platform
27. The components of a ML application
Vision
AI dev
kit
Vision
AI dev
kit